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Subclass representation-based face-recognition algorithm derived from the structure scatter of training samples

Subclass representation-based face-recognition algorithm derived from the structure scatter of training samples

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Representation-based face-recognition techniques have received attention in the field of pattern recognition in recent years; however, the well-known works focus mainly on constraint conditions and dictionary learning. Few researchers study, which sample data features determine the performance of representation-based classification algorithms. To address this problem, the authors define the structure-scatter degree, which represents the structural features of training sample sets, to determine whether a set is suitable for the representation-based classification algorithm. Experimental results show that sets with a higher structure scatter more likely allows a classification algorithm to obtain a higher recognition rate. Further, the block contribution degree (DBC) of a training sample set is defined to evaluate whether a sample set is suitable for block-based sparse-representation classification algorithms. Experimental results indicate that if the DBC approaches zero, the block technique is unlikely to improve the performance of a representation-based classification algorithm. Thus, they devise a self-adaptive optimisation method to generate an optimal block size, an overlapping degree, and a block-weighting scheme. Finally, they propose the structure scatter-based subclass representation classification. Experimental results demonstrate that the proposed algorithm not only improves the recognition accuracy of the representation-based classification algorithm, but also greatly reduces its time complexity.

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